Industrial equipment (e.g., machinery) is often equipped with sensors to provide insights regarding the ongoing performance and condition of the equipment to aid in decisions regarding maintenance and replacement of such equipment. Each piece of equipment may have several different sensors, each of which is responsible for providing output data related to different functional aspects of the equipment. Given the large quantity of sensor data output for each piece of equipment, it is often difficult to accurately interpret and understand the data that is output. For example, conventional methods involve performing calculations using the sensor data in accordance with a standard rubric. However, the conventional methodologies are very time consuming, and their result is simply a snapshot of the condition of the equipment at the time of the particular sensor data used in the calculations. As a result, the insights gained from the application of these conventional methodologies do not reflect the current condition of the equipment being analyzed.
Additionally, industrial equipment is often subject to periodic reviews (e.g., annual or quarterly) to assess performance and condition of the equipment. During such reviews, the condition of the equipment is evaluated to determine whether repairs or maintenance need to occur to avoid potentially lengthy downtime of the equipment due to wear or damage. The conventional review process again involves performing calculations using the sensor data in accordance with a standard rubric. However, it is often difficult to manually collect the sensor data, and the calculations of the conventional methodologies are often too complex to produce results that are both accurate and timely. Further, in many instances, it is important for a decision maker (e.g., a human tasked with maintaining equipment in a proper working order) to understand how sensor data output is used in the calculations and what the exact values are, though the conventional methodologies typically do not provide a convenient way for the decision makers to do this. Making decisions regarding maintenance and replacement of equipment without an accurate understanding of the current condition of the equipment and the calculations used to assess the current condition may ultimately compromise the health and longevity of the equipment.